Robotic Assistive Devices Research Articles

Integration of Forearm sEMG Signals with IMU Sensors for Trajectory Planning and Control of Assistive Robotic Arm.

Patients with issues such as cerebral palsy, spinal cord injury, and multiple sclerosis have difficulties with activities of daily living (ADL). Their abilities to perform tasks can be improved through vigorous physical therapy. When that therapy is either not effective or lacking in its progression an assistive robotic device can be used to improve patients' quality of life and help them in accomplishing ADL's. This study presents implementation of an EMG controlled assistive robotic arm to aid patients with upper limb mobility limitations. Using the MYO armband, EMG signals were obtained from three healthy human test subjects and were analyzed in MATLAB® Simulink®. Post signal acquisition, signals were classified to be used as inputs for a Kinova MICO 6 DOF manipulator. Trajectories are planned based on the user EMG signals and robot position data obtained from the Polhemus 6D motion tracker, an IMU-type sensor, which automatically provide position and orientation data. An inverse dynamics controller is developed to drive the robot joints accordingly. Results have shown that the classification accuracy of the EMG signals to control commands for the robot was greater than 90%. The classification accuracy was achieved through the use of a pattern recognition neural network. This preliminary investigation demonstrates the possible future implementation of the system for its intended application.

Effects of varying the rest period on the onset angle of lumbar flexion-relaxation in simulated sheep shearing: a preliminary study.

Wool harvesting remains an important industry in Australia, but its workers suffer from extreme rates of injury, in particular, the lower back injuries. Reducing injuries in sheep shearing could be as simple as extending shearer rest periods between sheep, but the effect of this has not previously been studied. The lumbar flexion-relaxation phenomenon is present in sheep shearing and the onset angle of this phenomenon can provide insight into lower back injury risk. The increase in the onset angle of lumbar flexion-relaxation over several work-rest periods for a simulated sheep shearing task is studied. The rate of increase in the onset angle of lumbar flexion-relaxation was higher when shorter breaks were taken for all participants at least unilaterally, indicating that longer rest breaks could reduce back injury risk. Due to the constraints of the sheep shearing occupation, this type of intervention is better suited to learner and novice shearers. Assistive robotic devices would be more suited to reduce injuries in expert shearers, and some insight is provided for the application of these within sheep shearing. Further study of this phenomenon in sheep shearing could provide additional insight to developing an assistive device that could reduce injury.

Voice Control Interface Prototype for Assistive Robots for People Living with Upper Limb Disabilities.

This paper presents a voice control interface prototype for assistive robots aiming to help people living with upper limb disabilities to perform daily activities autonomously. Assistive robotic devices can be used to help people with upper-body disabilities gain more autonomy in their daily life. However, it is very difficult or even impossible for certain users to control the robot with conventional control systems (e.g. joystick, sip-and-puff). This paper presents the design and preliminary evaluation of a voice command system prototype for the control of assistive robotic arms' movements. This work aims at making the control of assistive robots more intuitive and fluid, and to perform various tasks in less time and with a lesser effort. The prototype of the voice command interface developed is first presented, followed by two experiments with five able-bodied subjects in order to assess the system's performance and guide future development.

Muscle coordination during robotic assisted walking using Lokomat

In the past two decades, neurorehabilitation has been complemented with modern robotic assisted devices, such as the Lokomat for gait training. The Lokomat is a robotic gait trainer adjustable in g...

Longitudinal Analysis of Stroke Patients' Brain Rhythms during an Intervention with a Brain-Computer Interface.

Stroke is a leading cause of motor disability worldwide. Upper limb rehabilitation is particularly challenging since approximately 35% of patients recover significant hand function after 6 months of the stroke's onset. Therefore, new therapies, especially those based on brain-computer interfaces (BCI) and robotic assistive devices, are currently under research. Electroencephalography (EEG) acquired brain rhythms in alpha and beta bands, during motor tasks, such as motor imagery/intention (MI), could provide insight of motor-related neural plasticity occurring during a BCI intervention. Hence, a longitudinal analysis of subacute stroke patients' brain rhythms during a BCI coupled to robotic device intervention was performed in this study. Data of 9 stroke patients were acquired across 12 sessions of the BCI intervention. Alpha and beta event-related desynchronization/synchronization (ERD/ERS) trends across sessions and their association with time since stroke onset and clinical upper extremity recovery were analyzed, using correlation and linear stepwise regression, respectively. More EEG channels presented significant ERD/ERS trends across sessions related with time since stroke onset, in beta, compared to alpha. Linear models implied a moderate relationship between alpha rhythms in frontal, temporal, and parietal areas with upper limb motor recovery and suggested a strong association between beta activity in frontal, central, and parietal regions with upper limb motor recovery. Higher association of beta with both time since stroke onset and upper limb motor recovery could be explained by beta relation with closed-loop communication between the sensorimotor cortex and the paralyzed upper limb, and alpha being probably more associated with motor learning mechanisms. The association between upper limb motor recovery and beta activations reinforces the hypothesis that broader regions of the cortex activate during movement tasks as a compensatory mechanism in stroke patients with severe motor impairment. Therefore, EEG across BCI interventions could provide valuable information for prognosis and BCI cortical activity targets.

How Cognitive Models of Human Body Experience Might Push Robotics.

In the last decades, cognitive models of multisensory integration in human beings have been developed and applied to model human body experience. Recent research indicates that Bayesian and connectionist models might push developments in various branches of robotics: assistive robotic devices might adapt to their human users aiming at increased device embodiment, e.g., in prosthetics, and humanoid robots could be endowed with human-like capabilities regarding their surrounding space, e.g., by keeping safe or socially appropriate distances to other agents. In this perspective paper, we review cognitive models that aim to approximate the process of human sensorimotor behavior generation, discuss their challenges and potentials in robotics, and give an overview of existing approaches. While model accuracy is still subject to improvement, human-inspired cognitive models support the understanding of how the modulating factors of human body experience are blended. Implementing the resulting insights in adaptive and learning control algorithms could help to taylor assistive devices to their user's individual body experience. Humanoid robots who develop their own body schema could consider this body knowledge in control and learn to optimize their physical interaction with humans and their environment. Cognitive body experience models should be improved in accuracy and online capabilities to achieve these ambitious goals, which would foster human-centered directions in various fields of robotics.

Rehabilitation with robotic glove (Gloreha) in poststroke patients

Background and Objectives: Stroke is a leading cause of long-term disability. Rehabilitation involving repetitive, high-intensity, and task-specific exercise is the pathway to restore motor skills. Robotic assistive devices such as Gloreha are increasingly being used in upper limb rehabilitation. The aim of this study is to explore the efficacy of robotic therapy for upper limb rehabilitation using robotic glove (Gloreha) in patients with stroke. Materials and Methods: The patients affected by stroke who were admitted to our rehabilitation unit were studied. Patients were exposed to Gloreha device rehabilitation (30 min/die), physiotherapy (1,5 hours/die), and occupational therapy (30 min/die). We measured the impairment in motor function and muscle tone using the modified Ashworth scale (MAS), the activities of daily living functional independence measure (FIM), and the finger dexterity Nine-Hole Peg test (NHPT). Results: Twelve patients (mean age = 64.5 years; male/female: 8:4) were admitted at the rehabilitation training. We found statistically significant differences between admission and discharged in terms of functional recovery using the FIM scale (pre/M = 88.33; post/M = 117.25, P = 0.01); hand training showed a better outcome using the NHPT (pre/M = 51.8; post/M = 36.33, P = 0.01). No significant changes were observed in terms of spasticity with the MAS (pre/M = 1.25; post/M = 1.08;P > 0.05). Conclusions: Rehabilitation with robotic glove (Gloreha) can positively promote functional recovery of arm function in a patient with stroke.

Robot-Assisted Training in Laparoscopy Using Deep Reinforcement Learning

Minimally invasive surgery (MIS) is increasingly becoming a vital method of reducing surgical trauma and significantly improving postoperative recovery. However, skillful handling of surgical instruments used in MIS, especially for laparoscopy, requires a long period of training and depends highly on the experience of surgeons. This letter presents a new robot-assisted surgical training system which is designed to improve the practical skills of surgeons through intrapractice feedback and demonstration from both human experts and reinforcement learning (RL) agents. This system utilizes proximal policy optimization to learn the control policy in simulation. Subsequently, a generative adversarial imitation learning agent is trained based on both expert demonstrations and learned policies in simulation. This agent then generates demonstration policies on the robot-assisted device for trainees and produces feedback scores during practice. To further acquire surgical tools coordinates and encourage self-oriented practice, a mask region-based convolution neural network is trained to perform the semantic segmentation of surgical tools and targets. To the best of our knowledge, this system is the first robot-assisted laparoscopy training system which utilizes actual surgical tools and leverages deep reinforcement learning to provide demonstration training from both human expert perspectives and RL criterion.

The FreeD module for the Lokomat facilitates a physiological movement pattern in healthy people \u2013 a proof of concept study

BackgroundA contralateral pelvic drop, a transverse rotation and a lateral translation of the pelvis are essential features of normal human gait. These motions are often restricted in robot-assisted gait devices. The optional FreeD module of the driven gait orthosis Lokomat (Hocoma AG, Switzerland) incorporates guided lateral translation and transverse rotation of the pelvis. It consequently should support weight shifting during walking. This study aimed to investigate the influence of the FreeD module on trunk kinematics and hip and trunk muscle activity.MethodsThirty- one healthy adults participated. A video analysis of their trunk movements was performed to investigate the lateral chest and pelvis displacement within the Lokomat (with and without FreeD), and this was compared to treadmill walking. Furthermore, surface electromyography (sEMG) signals from eight muscles were collected during walking in the Lokomat (with and without FreeD), on the treadmill, and overground. To compare the similarity of the sEMG patterns, Spearman’s correlation analyses were applied.ResultsWalking with FreeD elicited a significantly higher lateral pelvis displacement and a lower lateral chest displacement (relative to the pelvis) compared to walking with a fixated pelvis. No significant differences in the sEMG patterns were found for the Lokomat conditions (with and without FreeD) when comparing it to treadmill or overground walking.ConclusionsThe differences in pelvis displacement act as a proof of concept of the FreeD module. The reduction of relative lateral chest movement corresponds to a decrease in compensatory trunk movements and has its origin in allowing weight shifting through the FreeD module. Both Lokomat conditions showed very similar muscle activity patterns of the trunk and hip compared to overground and treadmill walking. This indicates that the Lokomat allows a physiological muscle activity of the trunk and hip during gait.

Feel-Good Robotics: Requirements on Touch for Embodiment in Assistive Robotics.

The feeling of embodiment, i.e., experiencing the body as belonging to oneself and being able to integrate objects into one's bodily self-representation, is a key aspect of human self-consciousness and has been shown to importantly shape human cognition. An extension of such feelings toward robots has been argued as being crucial for assistive technologies aiming at restoring, extending, or simulating sensorimotor functions. Empirical and theoretical work illustrates the importance of sensory feedback for the feeling of embodiment and also immersion; we focus on the the perceptual level of touch and the role of tactile feedback in various assistive robotic devices. We critically review how different facets of tactile perception in humans, i.e., affective, social, and self-touch, might influence embodiment. This is particularly important as current assistive robotic devices – such as prostheses, orthoses, exoskeletons, and devices for teleoperation–often limit touch low-density and spatially constrained haptic feedback, i.e., the mere touch sensation linked to an action. Here, we analyze, discuss, and propose how and to what degree tactile feedback might increase the embodiment of certain robotic devices, e.g., prostheses, and the feeling of immersion in human-robot interaction, e.g., in teleoperation. Based on recent findings from cognitive psychology on interactive processes between touch and embodiment, we discuss technical solutions for specific applications, which might be used to enhance embodiment, and facilitate the study of how embodiment might alter human-robot interactions. We postulate that high-density and large surface sensing and stimulation are required to foster embodiment of such assistive devices.

User-Adaptive Assistance of Assistive Knee Braces for Gait Rehabilitation

Patients suffering from neurological and orthopedic diseases or injuries usually have mobility impairment problems, and they require customized rehabilitation training to recover. In recent years, robotic assistive devices have been widely studied for gait rehabilitation. In this paper, methods to determine user-adaptive assistance of assistive knee braces (AKBs) in gait rehabilitation are investigated. A fuzzy expert system, which takes a patient's physical condition and gait analysis results as inputs, is proposed to configure suitable levels of different assistive functions of the AKB. During gait rehabilitation, the AKB generates a reference knee trajectory according to the patient's individual gait pattern, and the interaction force is controlled through a hybrid impedance controller considering the individual assistive function configuration. The proposed methods are verified through clinical pilot studies of a patient with lower limb weakness. Experimental results show that AKB with the proposed control strategies can provide effective assistance to improve the patient's gait performance during gait rehabilitation.

Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG

Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user’s intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.

Big data and deep learning in preventive and rehabilitation medicine

The digitalization in medicine has led to almost universal availability of information to different healthcare professionals and accelerated clinical pathways. Fast-track concepts and short hospital stays require intelligent and practicable systems in preventive and rehabilitation medicine. This includes optimization of movement analysis by innovative tools such as detectors sensing skin movements, portable feedback systems for monitoring, robot-assisted devices, and prevention programs based on reliable data. Finally, clinical structures are needed to exploit the maximal potential of artificial intelligence (AI) and deep learning. One example is the establishment of inter- and transdisciplinary professional teams such as aRehaBoard. In contrast to other cost-intensive disciplines such as oncology, the introduction of AI into rehabilitation orthopedics and trauma surgery with the support of cross-sectoral cooperation has great potential for performing well in patient benefit-orientated competition (value-based competition).

Using functional near infrared spectroscopy (FNIRS) to assess the effect of transcranial direct-current stimulation (TDCS) on spinal cord injury patient, during robot-assisted gait

Neuroplasticity is believed to be an important mechanism related to motor improvement of spinal cord injury (SCI) during rehabilitation program, which may be enhanced using transcranial direct-current stimulation (tDCS). The better understanding of this process is important for the improvement of rehabilitation therapy since it can be used as a prognostic biomarker, surrogate outcome and to guide the parameters of tDCS. Functional Near Infrared Spectroscopy (fNIRS) is an instrument to measures hemodynamic responses in the brain, which allows studying functional brain connectivity reorganization. So, this study aims to demonstrate the feasibility of fNIRS to evaluate real-time activation of motor cortex SCI patients during robot-assisted gait. Besides, to compare changes induced by active vs. sham tDCS. It is a preliminary analysis of SCI patients enrolled in a randomized double-blind clinical trial, with two arms (active tDCS vs. sham tDCS). Both groups received 30 sessions of tDCS for 20 minutes immediately before gait training with robotic-assisted device Lokomat-Pro ® (Hocoma). For fNIRS, 32 optodes was positioned parallel to the coronal plane. Software and hardware are from NIRX Medical Technologies. The differential hemoglobin index at rest (standing) and during movement were evaluated using paired T-test. The null hypothesis was that during gait, metabolic consumption would increase in the primary motor cortex (M1). Patients had an enhanced activation of M1, next to Cz, C1 and C2 (10-20 EEG system) during gait, when compared with standing position. This activation seems to be higher in active group comparing to sham group ( Fig. 1 ). This study shows the viability of use fNIRS to measure brain activity of SCI patients during robot-assisted gait. Future analyses, with a larger sample, are necessary to confirm these findings and correlate it with clinical outcomes.

Assisting hand function after spinal cord injury with a fabric-based soft robotic glove

BackgroundSpinal cord injury is a devastating condition that can dramatically impact hand motor function. Passive and active assistive devices are becoming more commonly used to enhance lost hand strength and dexterity. Soft robotics is an emerging discipline that combines the classical principles of robotics with soft materials and could provide a new class of active assistive devices. Soft robotic assistive devices enable a human-robot interaction facilitated by compliant and light-weight structures. The scope of this work was to demonstrate that a fabric-based soft robotic glove can effectively assist participants affected by spinal cord injury in manipulating objects encountered in daily living.MethodsThe Toronto Rehabilitation Institute Hand Function Test was administered to 9 participants with C4-C7 spinal cord injuries to assess the functionality of the soft robotic glove. The test included object manipulation tasks commonly encountered during activities of daily living (ADL) and lift force measurements. The test was administered to each participant twice; once without the assistive glove to provide baseline data and once while wearing the assistive glove. The object manipulation subtests were evaluated using a linear mixed model, including interaction effects of variables such as time since injury. The lift force measures were separately evaluated using the Wilcoxon signed-rank test.ResultsThe soft robotic glove improved object manipulation in ADL tasks. The difference in mean scores between baseline and assisted conditions was significant across all participants and for all manipulated objects. An improvement of 33.42 ± 15.43% relative to the maximal test score indicates that the glove sufficiently enhances hand function during ADL tasks. Moreover, lift force also increased when using the assistive soft robotic glove, further demonstrating the effectiveness of the device in assisting hand function.ConclusionsThe results gathered in this study validate our fabric-based soft robotic glove as an effective device to assist hand function in individuals who have suffered upper limb paralysis following a spinal cord injury.

Biomechanics and energetics of walking in powered ankle exoskeletons using myoelectric control versus mechanically intrinsic control

BackgroundControllers for assistive robotic devices can be divided into two main categories: controllers using neural signals and controllers using mechanically intrinsic signals. Both approaches are prevalent in research devices, but a direct comparison between the two could provide insight into their relative advantages and disadvantages. We studied subjects walking with robotic ankle exoskeletons using two different control modes: dynamic gain proportional myoelectric control based on soleus muscle activity (neural signal), and timing-based mechanically intrinsic control based on gait events (mechanically intrinsic signal). We hypothesized that subjects would have different measures of metabolic work rate between the two controllers as we predicted subjects would use each controller in a unique manner due to one being dependent on muscle recruitment and the other not.MethodsThe two controllers had the same average actuation signal as we used the control signals from walking with the myoelectric controller to shape the mechanically intrinsic control signal. The difference being the myoelectric controller allowed step-to-step variation in the actuation signals controlled by the user’s soleus muscle recruitment while the timing-based controller had the same actuation signal with each step regardless of muscle recruitment.ResultsWe observed no statistically significant difference in metabolic work rate between the two controllers. Subjects walked with 11% less soleus activity during mid and late stance and significantly less peak soleus recruitment when using the timing-based controller than when using the myoelectric controller. While walking with the myoelectric controller, subjects walked with significantly higher average positive and negative total ankle power compared to walking with the timing-based controller.ConclusionsWe interpret the reduced ankle power and muscle activity with the timing-based controller relative to the myoelectric controller to result from greater slacking effects. Subjects were able to be less engaged on a muscle level when using a controller driven by mechanically intrinsic signals than when using a controller driven by neural signals, but this had no affect on their metabolic work rate. These results suggest that the type of controller (neural vs. mechanical) is likely to affect how individuals use robotic exoskeletons for therapeutic rehabilitation or human performance augmentation.

SAM, an Assistive Robotic Device Dedicated to Helping Persons with Quadriplegia: Usability Study

The purpose of this case study was to evaluate the technology readiness level of a prototype named “SAM” that consists of a robotic arm mounted on a mobile base. Usability and acceptance assessments were performed in patients with high-level quadriplegia. Seventeen patients with quadriplegia were trained to pick up three different objects in three different situations amounting to scenarios 1, 2 and 3. Each scenario was observed over the 5 steps of its execution. For each step, usability and acceptability parameters were measured. The success rate was optimal or acceptable (70–100%) for (step 1) identifying the room where the object was located, (step 2) directing SAM towards the object and (step 5) monitoring the return of SAM and dropping the object. Designating and validating the object (step 3), approaching and grasping the object (step 4) were rarely completed without any mistake. A majority of patients (70.6%) saw the usage of SAM as an interesting perspective for daily tasks (58.8%) as well as in the potential reorganising of the caregivers’ time (47%). This study suggests that the usage of SAM allows patients with quadriplegia to grab objects both within and out of their field of view. Possibilities allowing the act of seizure are increased via a user-friendly interface which is yet to be improved. Its technology readiness level has been estimated 5.

T107. Using Functional near Infrared Spectroscopy (fNIRS) to assess brain activity of spinal cord injury patient, during robot-assisted gait

Introduction Neuroplasticity and functional brain connectivity reorganization is believed to be an important mechanism related to motor improvement of spinal cord injury (SCI) during rehabilitation program. The better understanding of this process is important for the improvement of rehabilitation therapy since it can be used as a prognostic biomarker, surrogate outcome and to guide new types of interventions, such as non-invasive brain stimulation. There is important insight from fMRI studies about cerebral changes related to SCI, however, these studies have an important limitation, since it is performed with patient lying down and in a restrict position. The functional Near Infrared Spectroscopy (fNIRS), on the other hand, allows to measure brain activity during gait in a naturalistic environment. Moreover, robot-assisted gait training allows SCI patient to perform active-assisted gait mimicking the physiological pattern. So, this study aims to demonstrate the feasibility of fNIRS to evaluate real-time activation of motor cortex in three SCI patients during robot-assisted gait. Methods The data used in this study are from three SCI patients that are enrolled in a clinical trial (ClinicalTrials.gov Identifier: NCT02562001). Was used portable fNIRS with 32 optodes, 16 emitters and 16 detectors, positioned parallel to the coronal plane, through the Cz (10–20 EEG system). This assembly has primary focus in the primary motor cortex (M1) of both cerebral hemispheres. Before each evaluation, an automatic calibration process was performed using NIRStar software to determine an optimum amplification factor of 0.4–4.0 V for each channel (emitter-detector pair). Data preprocessing was performed with NIRSLab software. Software and hardware (NIRSport) are from NIRX Medical Technologies, Glen Head, New York. The relative changes in the concentration of oxy and deoxyhemoglobin was evaluated for each condition (standing and walking), with a duration of 30 s for each block and an interval of 5 s between each block. For the gait protocol was used robotic-assisted device Lokomat Pro (Hocoma; Zurich, Switzerland). An average containing the five trial blocks for each condition was performed to improve the signal-to-noise ratio. The differential hemoglobin index (oxyhemoglobin concentration - deoxyhemoglobin) at rest (standing) and during movement were evaluated using paired T-test. The null hypothesis was that during gait, metabolic consumption would increase in the primary motor cortex. Results Two of the patients had an enhanced activation of M1, next to Cz, C1 and C2 (10–20 EEG system) during gait, when compared with standing position. One of the patients did not present changes in M1 brain activity. Conclusion This study shows the viability of use fNIRS to measure brain activity of SCI patients during robot-assisted gait. Future analyses, with a larger sample, are necessary to correlate this findings with clinical outcomes.

Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond.

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.

Prototyping and evaluation of interactive and customized interface and control algorithms for robotic assistive devices using Kinect and infrared sensor

Prototyping and evaluation of interactive and customized interface and control algorithms for robotic assistive devices using Kinect and infrared sensor